Comp F3 White Paper planning

US/Pacific
Description
    • 08:00 08:05
      Introduction/WP schedule 5m
    • 08:05 08:10
      WP update 5m
      Speaker: Brian Nord (Fermilab)
    • 08:10 08:15
      GNN/Instance Segmentation WPs 5m
      Speaker: Savannah Thais (Princeton University)
    • 08:15 08:20
      WP on Detector Simulation 5m
      Speaker: Mikuni Vinicius (LBNL)
    • 08:20 08:25
      Symmetry Group Equivariant Architectures 5m
      Speaker: Mariel Pettee (Yale University)
    • 08:30 08:35
      Data Science/ML education in HEP 5m
      Speaker: Mark Neubauer (University of Illinois at Urbana-Champaign)
    • 08:40 08:45
      Self-Driving System for Physics 5m
      Speaker: Cecilia Tosciri
    • 08:45 08:50
      Applications of Machine Learning to Lattice Quantum Field Theory 5m
      Speakers: Daniel Hackett (University of Colorado, Boulder), Daniel Hackett
    • 08:50 08:55
      Model Independent Searches for New Physics using Machine Learning 5m

      Our main idea is to assess the performance of different semi-supervised or unsupervised methods using different benchmark data sets. Besides commonly explored multi-jet signatures, we also want to consider multi-lepton processes. In order to compare the performance of different methods, we also consider several types of data input, e.g., kinematic variables and image data sets. This will provide a comparison of different methods on the same footing.

      Speaker: Charanjit Kaur
    • 08:55 09:00
      Community Needs, Tools and Resources 5m
      Speaker: Dylan Rankin (Massachusetts Institute of Technology)